Informatica Recognized as a Leader for 18th Year in a Row in the 2023 Gartner® Magic Quadrant™ for Data Integration Tools

Last Published: Dec 06, 2023 |
Pratik Parekh
Pratik Parekh

SVP Products and General Manager 

I am thrilled to announce that Informatica has once again been acknowledged as a Leader in the 2023 Gartner Magic Quadrant for Data Integration Tools, marking our eighteenth consecutive year of recognition. We believe this is a strong testament to our consistent multi-year focus on innovating data integration and data engineering capabilities to adapt to changing market trends. Gartner evaluated 21 data integration vendors and positioned Informatica at the furthest on the Completeness of Vision and highest for Ability to Execute axes.

Figure 1: Gartner Magic Quadrant for Data Integration Tools

Criteria for Gartner Data Integration Magic Quadrant

Gartner evaluated these vendors across a variety of data integration use cases, core critical capabilities, target audiences and delivery models as outlined below.

Core use cases: data engineering, cloud data integration, operational data integration, master data management (MDM) and data fabric design support.

Core critical capabilities: bulk/batch data movement, data replication and synchronization, data virtualization, stream data integration, advanced data transformation and data API services.

Target audiences: data engineers, data integration developers and citizen integrators supporting business functions/domains.

Delivery models: on-premises, private cloud, public cloud, multi-cloud and hybrid cloud.

Why is Data Integration critical for the success of Artificial Intelligence (AI)?

AI and machine learning (ML) initiatives have immense potential, but they also face various challenges that can hinder their successful implementation. One of the most significant challenges is the availability of high-quality, trusted, governed, and holistic data, which is extremely critical for the success of any AI/ML project. AI and ML algorithms rely heavily on data, and poor quality or insufficient data can lead to inaccurate results. Therefore, having high-quality data is crucial for the success of any AI/ML project.

To succeed in AI, organizations must establish a robust data integration foundation that provides clean and trusted data pipelines. This will enable organizations to gain reliable insights from analytics and AI, which can help improve productivity, facilitate faster decision-making, and gain a competitive advantage.

Adopting a comprehensive and intelligent data management platform with data integration capabilities is crucial for successful AI. These capabilities include data replication, data ingestion, CDC, DataOps/MLOps, ELT, data governance, data catalog, and data quality. By fully utilizing the value of data, organizations can improve efficiency, reduce costs, and increase productivity - all of which are essential in today's digital economy.

Informatica’s Data Integration and Data Engineering Vision and Strategy

Our vision is to build an easy, efficient and cost-effective data integration and data engineering platform for everyone and everywhere, powered by CLAIRE – our AI-powered metadata intelligence engine. We support the following data integration patterns today, as shown in Figure 2.

Figure 2: Informatica’s Data Integration and Data Engineering Vision 

  • Data Replication -Modernize your data by replicating and ingesting virtually any data and pattern at scale with an easy-to-use, low-code/no-code wizard-based experience.
  • Change Data Capture (CDC) - Identify, copy and replicate incremental real-time changes from the source databases or applications into a cloud data warehouse or data lake for analytics and AI consumption.
  • ELT – Seamlessly extract data from various sources – databases, applications, files and streaming – into a cloud data warehouse and cloud data lake and then transform it with advanced pushdown optimization capabilities that can elevate developer productivity and cut costs.
  • ETL/Reverse ETL – Build data pipelines using a low-code/no-code tool to extract data from the data sources and then cleanse, enrich, transform and finally load data into a cloud data warehouse for analytic consumption. Data engineers can also do reverse ETL by moving consolidated data from a data warehouse to frontline applications.
  • INFACore – Simplify the development and maintenance of complex data pipelines by turning thousands of lines of code into a single function with industry’s first simple, open, extensible, and embeddable intelligent headless data management without any context switching.
  • ModelServe – Put AI into action by operationalizing high-quality and governed AI/ML models using virtually any tool, framework or data science platform, at scale, within minutes instead of days and months.
  • Advanced Cloud Data Integration - Optimize, govern, and control cloud costs with FinOps-powered data integration across the entire data engineering lifecycle, from data pipeline development to production.
  • CDI-PC - Enable customers to run PowerCenter (PC) workloads as IDMC workloads in PC-compatible mode, without needing metadata or business logic migration. This benefits our customers immensely through automated updates, real-time security fixes and reducing/eliminating overheads that are involved in operating on-premises software.
  • Elastic/Serverless – Drive data engineering and data science projects at scale with Informatica’s elastic and serverless capabilities, improving data engineer’s productivity and optimizing cost. Data engineers don’t need to administer the servers and instead spend their time on more high-value tasks, like building business logic and running applications, rather than worry about scalability or server provisioning and maintenance.
  • API Center - Design, implement, deploy, monitor, deprecate, secure, discover, reuse and retire APIs that span multiple clouds and on-premises systems within and outside their firewalls

Next Steps

Find out more about our strengths. Download your complimentary copy of the 2023 Gartner® Magic Quadrant™ for Data Integration Tools now.


Gartner, Magic Quadrant for Data Integration Tools, Ehtisham Zaidi, Robert Thanaraj, Sharat Menon, Thornton Craig, Roxane Edjlali, Michele Launi, 4 December 2023.

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First Published: Dec 06, 2023